Autonomous rail vehicle movement and system among a group of vehicles on a rail system

ABSTRACT

An autonomous vehicle (“AV”) is configured on a railway system. The AV can be configured among the other vehicles and railway to communicate with a rider on a peer to peer basis to pick up the rider on demand, rather than the rider being held hostage to a fixed railway schedule. The rider can have an application on his/her cell phone, which tracks each of the AVs, and contact them using the application on the cell phone.

BACKGROUND

The present invention relates to techniques, including a method, andsystem, for movement of an autonomous vehicle on a railway system amonga plurality of vehicles using a combination of sensing and artificialintelligence techniques to monitor, detect, and act on activities. In anexample, management of such vehicle can be from both active and passivesensors, among others. Merely by way of examples, various applicationscan include daily life, and others.

Motor vehicles have greatly progressed from the early days in the late1880's by Karl Benz with gasoline-powered engines, then in 1908 by theintroduction of the Model T by Henry Ford and the Ford Motor Company,and most recently with electric cars manufactured by Tesla, Inc. of PaloAlto. Most recently, automated or autonomous vehicles (AVs) have beenintroduced with continuous, or near continuous, sensor data gatheringand processing in order to operate safely through real-worldenvironments. In doing so, many AVs include sensor arrays that havemultiple sensor systems. For example, AV sensor arrays can include anynumber of active sensor systems, such as electromagnetic signal rangingand detection systems (e.g., radar and LiDAR systems). The AV sensorarrays can also include passive sensor systems, such as stereo camerasystems or proximity sensors. In order for the AV to operate safely andreliably, the quality (e.g., the signal to noise ratio) of the sensordata collected by these sensor systems may be crucial.

Although motor vehicles have progressed, we still face limitations withthe basic technology of vehicles configured for railways, which areknown as trains.

SUMMARY

According to the present invention, techniques related to a method, andsystem, for movement of an autonomous vehicle on a railway system amonga plurality of vehicles are provided. In particular, the invention canuse a combination of sensing and artificial intelligence techniques tomonitor, detect, and act on activities are provided. In an example,management of such vehicle can be from both active and passive sensors,among others. Merely by way of examples, various applications caninclude daily life, and others.

In an example, the autonomous vehicle (“AV”) can be configured among theother vehicles and railway to communicate with a rider on a peer-to-peerbasis to pick up the rider on demand, as illustrated in FIG. 1, ratherthan the rider being held hostage to a fixed railway schedule. The ridercan have an application on his/her cell phone, which tracks each of theAVs, and contact them using the application on the cell phone.

In an example, the method includes initiating movement of the autonomousvehicle configured in a rail of the railway system. In an example, theautonomous vehicle comprises a sensor array system configured spatiallyon the autonomous vehicle (AV). In an example, the sensor array systemcomprises a plurality of active sensor systems, among other elements.The active sensor systems can have at least one processor device coupledto the sensor array system. The systems can include a memory devicecoupled to the processing device.

In an example, the memory has various instructions stored or burned intothe memory. In an example, the memory has an instruction stored on thememory device. In an example, the instruction when executed by theprocessor causes the sensor array system to, as the AV travels a currentroute on the rail of the railway track system, dynamically detect areflectance of an event from a plurality of events, or other entities.In an example, the event can be selected from an anomaly, a stationaryfeature, or a location of one of the other plurality of vehicles, amongother detectable events. Other instructions can also be included.

In an example, the method also includes using data from the reflectanceof the event or the plurality of events to adjust a movement of the AVin relationship to the event, while the AV is mechanically disconnectedfrom the plurality of vehicles configured on the rail of the railwaysystem.

In an example, the present system is configured to create a demandschedule, rather than a fixed schedule often present in a railway, orpod, or track system.

Further details of the present method and associated systems can befound throughout the present specification and more particularly below.

The above examples and implementations are not necessarily inclusive orexclusive of each other and may be combined in any manner that isnon-conflicting and otherwise possible, whether they be presented inassociation with a same, or a different, embodiment or example orimplementation. The description of one embodiment or implementation isnot intended to be limiting with respect to other embodiments and/orimplementations. Also, any one or more function, step, operation, ortechnique described elsewhere in this specification may, in alternativeimplementations, be combined with any one or more function, step,operation, or technique described in the summary. Thus, the aboveexamples implementations are illustrative, rather than limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified illustration of an autonomous railway systemaccording to an example of the present invention.

FIG. 2 is a simplified illustration of a system diagram of for each ofthe autonomous vehicles according to an example of the presentinvention.

FIG. 3 is a simplified illustration of a LIDAR system according to anexample of the present invention.

FIG. 4 is a simplified illustration of a sensor array according to anexample of the present invention.

FIG. 5 is a simplified illustration of a sensor array according to analternative example of the present invention.

FIG. 6 is a simplified flow diagram of a method according to an exampleof the present invention.

DETAILED DESCRIPTION OF THE EXAMPLES

According to the present invention, techniques related to a method, andsystem, for movement of an autonomous vehicle on a railway system amonga plurality of vehicles are provided. In particular, the invention canuse a combination of sensing and artificial intelligence techniques tomonitor, detect, and act on activities are provided. In an example,management of such vehicle can be from both active and passive sensors,among others. Merely by way of examples, various applications caninclude daily life, and others.

In an example, the present invention provides a method for moving anautonomous vehicle 101 among a plurality of vehicles 103 configured on arailway track system 105, as shown in FIG. 1. As shown, a rider cancontact the vehicle and call it using an application on a cell phone. Inan example, the call is initiated, connected, and provided using awireless transmission 109 technique, such as cellular, WiFi, or others.In an example, the communication can be routed through the Internet 111and controlled under a plurality of servers 113 coupled to a memoryresource. In an example, the memory resource 115 can include, amongothers, dynamic random access memory, read only memory, Flash memory,fixed memory, optical memory, and any combination of these. Of course,there can be other variations, modifications, and alternatives.

In an example, the vehicle can be a rail car, trolley, or other vehicleor movable entity on a fixed track or cable system, among others. In anexample, each of the vehicles can have a human driver or more preferablycan be operated without a human driver.

In an example, the method includes initiating movement of the autonomousvehicle configured in a rail of the railway system. In an example, theautonomous vehicle comprises a sensor array system, as shown in FIG. 2,configured spatially on the autonomous vehicle (AV). In an example, thesensor array system comprises a plurality of active sensor systems,among other elements. The active sensor systems can have at least oneprocessor device (or processing device) 201 coupled to the sensor arraysystem 203, 205, 209, 211. The array has a camera or imaging capturedevice, a LiDAR (to be explained in more detail below), an accelerometer(or gyro or both), a location sensor, such as a Global PositioningSensor, and other sensor devices. The systems can include a memorydevice 215 coupled to the processing device. The system also has acommunication interface 207 and communication devices. Such interfaceand devices can include, among others, a variety of techniques such asBluetooth, WiFi, cellular (e.g., LTE, 5G), among other wirelesstechniques, and wired techniques such as Ethernet, Cable, and others,including combinations thereof.

In an example, the memory device can be one or more memories including afixed disk memory, a Flash memory, a dynamic random access memory, orother memory resources. In an example, the memory has variousinstructions stored or burned into the memory. In an example, the memoryhas an instruction stored on the memory device. In an example, theinstruction when executed by the processor causes the sensor arraysystem to, as the AV travels a current route on the rail of the railwaytrack system, dynamically detect a reflectance of an event from aplurality of events, or other entities. In an example, the event can beselected from an anomaly, a stationary feature, or a location of one ofthe other plurality of vehicles, among other detectable events. Otherinstructions can also be included.

In an example, the method also includes using data from the reflectanceof the event or the plurality of events to adjust a movement of the AVin relationship to the event, while the AV is mechanically disconnectedfrom the plurality of vehicles configured on the rail of the railwaysystem.

In an example, the method also includes monitoring each of the AVs usinga central database in real time, while collecting information associatedeach AVs in the central database.

The AV further comprises a relationship table comprising a plurality ofsensor configurations for each respective one of a plurality of activesensor systems included in the sensor array system. The plurality ofsensor configurations can be adjusted within a time frame at least oneoutput sensor configuration for the sensor array to identify the eventusing the detected reflectance of the event. In an example, theplurality of sensor configurations can also provide an output toinfluence movement of the AV based upon the detected reflectance andidentified event as the AV travels the current route.

Depending upon the example, sensor array system comprises alight-detection and ranging (LiDAR) system as shown in FIG. 3, or othersystem suitable for transmitting a signal and receiving a reflectionwith accuracy. In an example, the output sensor configuration configuresone or more of a scan rate of the LiDAR system, a photodetectorsensitivity of the LiDAR system, or a laser power level of the LiDARsystem. Of course, there can be other variations, modifications, andalternatives.

An example of a LiDAR system can be found in U.S. Pat. No. 7,969,558 inthe name of Hall issued Jun. 28, 2011, and titled “High Definition LidarSystem,” which is incorporated by reference herein, in its entirety forall purposes. In an example, the system 300 as shown in FIG. 3 has acommon bus 301. Various components include a plurality of input/outputinterfaces 307, 309, 313. Each of the interfaces can include an analogfront end, including a filter. In an example, the system has a pluralityof lasers 303.

In an example, the system has a plurality of external sensors 311configured to receive feedback from a reflection from an entity from oneor more of the lasers. In an example, the system has a plurality ofinternal sensors 305 for adjusting or providing compensation for theplurality of external sensors from movement of the LiDAR system. In anexample, the system has a communication interface 315, which can includea physical connection, wireless connection, or an optical connection.Other elements include a memory resource 319, digital signal processor,DSP 317, and optionally a micro-processor device 321. The system alsohas a common interface for an input/output interface 323, as shown.

In an example, the LiDAR (or Laser Imaging Detection and Ranging)terrain mapping and obstacle detection system is employed as a sensorfor an autonomous vehicle. The system includes 8 assemblies of 8 laserseach or 2 assemblies of 32 lasers each forming a 64-element LiDARsystem, although there can be variations. The system has a 360-degreehorizontal field of view (FOV) and a 26.8-degree vertical FOV. Thesystem is typically mounted on the top center of a vehicle, giving it aclear view in all directions, and rotates at a rate of up to 200 Hz,thereby providing a high point cloud refresh rate, such a high ratebeing necessary for autonomous navigation at higher speeds. At thisconfiguration, the system can collect approximately 1 million time offlight (TOF) distance points per second. The system provides the uniquecombination of 360 degree FOV, high point cloud density, and highrefresh rate. The standard deviation of TOF measurements is equal to orless than 5 centimeters. The system has an inertial navigation system(INS) sensor system mounted on it to report exact pitch and roll of theunit that is used by navigational computers to correct for thesedeviations. The unit generates its own light and uses a proprietaryfilter to reject sunlight, so it works well under all lighting and mostweather conditions. Through the use of digital signal processor (DSP)control, a dynamic power feature allows the system to increase theintensity of the laser emitters if a clear terrain reflection is notobtained by photo detectors (whether due to reflective surface, weather,or other reasons), and to reduce power to the laser emitters for safetyreasons if a strong reflection signal is detected by photo detectors. Adirect benefit of this feature is that the system is capable of seeingthrough fog and heavy rain by increasing laser power dynamically andignoring early reflections. The unit also has the capability to receiveand decipher multiple returns from a single laser emission throughdigitization and analysis of the waveform generated by the detector asthe signal generated from the emitter returns.

In an example, the system sends data in the form of range and intensityinformation via Ethernet output (or similar output) to a masternavigational system. Using standard trigonometry, the range data isconverted into x and y coordinates and a height value. The height valueis corrected for the vehicle's pitch and roll so the resulting map iswith reference to the horizontal plane of the vehicle. The map is then“moved” in concert with the vehicle's forward or turning motion. Thus,the sensor's input is cumulative and forms an ultra-high-density profilemap of the surrounding environment.

In an example, the highly detailed terrain map is then used to calculateobstacle avoidance vectors if required and, as importantly, determinethe maximum allowable speed given the terrain ahead. The systemidentifies of size and distance of objects in view, including thevertical position and contour of a road surface. The anticipated offsetof the vehicle from a straight, level path, either vertical orhorizontal, at different distances is translated into the G-force thatthe vehicle will be subject to when following the proposed path at thecurrent speed. That information can be used to determine the maximumspeed that the vehicle should be traveling, and acceleration or brakingcommands are issued accordingly. In all cases the software seeks thebest available road surface (and thus the best possible speed) stillwithin the boundaries of a global positioning system (GPS) waypointbeing traversed. Further details of an example of a system can be foundin the aforementioned U.S. patent, among others. Of course, there can beother variations, modifications, and alternatives.

In an example, the sensor array system comprises a radar system. In anexample, the output sensor configuration configures a pulse width of acarrier signal of the radar system. In an example, each of the pluralityof active sensor systems emit one or more of sounds waves orelectromagnetic waves.

In one example, the executed instruction causes the system todynamically determine the one or more output sensor configurations byperforming a lookup in the relationship table based on a reflectance ofeach of the detected reflectance events.

In an example, the executed instruction causes the system to dynamicallydetermine the one or more output sensor configurations by performing anoptimization utilizing a plurality of possible configurations for eachof the plurality of active sensor systems based on a reflectance of eachof the detected reflectance events by identifying a surface feature ofeach of the events. The optimization uses a fitting function to convergeon the one or more sensor output configurations in the relationshiptable. Again, there can be other variations, modifications, andalternatives.

In an example, the present invention provides the plurality of activesensor systems that comprise a LiDAR system and a radar system. In anexample, the sensor array further includes a plurality of passive sensorsystems that detect reflected natural light. In an example, both passiveand active sensor systems are included.

In an example, the at least one of passive sensor systems of the sensorarray comprise a stereo camera system. The stereo camera system recordsand captures both images and audio.

In an example, the LiDAR system, the radar system, and the stereo camerasystem each provides sensor data to a control system of the AV to enablethe AV to maneuver along the current route and initiate adjustment ofthe movement of the AV along the track.

In an example, the control system dynamically processes data from thesensor array system to increase speed, reduce speed, or stop the AValong the current route or wherein the control system interfaces withthe central database to increase speed, reduce speed, or stop the AValong the current route. The detected reflectance events can compriseone or more surface features of an environment around the AV and one ormore weather features indicating precipitation.

In an example, the executed instruction further causes the predictivesensor array configuration system to: in response to identifying theweather features indicating precipitation, deprioritize sensor data fromthe LiDAR system for processing by the control system. Of course, therecan be other variations. Additionally, the executed instruction furthercauses the system to: in response to identifying the weather featuresindicating precipitation, deactivate the LiDAR system. The executedinstruction causes the system to dynamically identify the one or morereflectance events by receiving reflectance data from a number ofproximate AVs traveling on the current route on the railway system. Inother examples, the executed instruction causes the system to receivethe reflectance data from the proximate AVs by establishing a meshnetwork with the proximate AVs on the railway system or other entitiesthat are not AVs. In an example, the executed instruction further causesthe system to: maintain a sub-map database comprising 3D surface data ofan operational region of the AV on the railway system; and identify,using a current position of the AV, a correlated sub-map from thesub-map database that provides 3D surface data surrounding the currentposition of the AV; wherein the executed instruction causes the systemto dynamically identify the reflectance events that affect detectabilityby the sensor array from the 3D surface data provided by the correlatedsub-map.

In an example, the system can also have a variety of stationaryfeatures. In an example, the stationary feature can be one or more of arailway sign, a railway station, a railway track, a vehicle roadway, arailway crossing, or other fixture, among other things. In an example,the stationary feature can also be the location of one of the otherplurality of vehicles comprising a distance between the other vehicleand the AV, the anomaly being one or more of a human being, a dog, acat, a horse, cattle, a moving vehicle crossing the railway track, aweather condition, or a defect on the railway track. In an example, thestationary feature can also be movable or moving.

In an example, FIG. 4 is a simplified illustration of a sensor arrayaccording to an example of the present invention. In an alternativeexample, FIG. 5 is a simplified illustration of a sensor array accordingto the alternative example of the present invention. In an example, eachof the systems 400, 500 is for adjusting movement of an autonomousvehicle among a plurality of vehicles configured on a railway tracksystem.

In an example, the system has a sensor array system configured spatiallyon the autonomous vehicle (AV). In an example, the sensor array system403 comprises a plurality of active sensor systems 409 and a pluralityof passive sensor systems 407. In an example, at least one processordevice 411 is coupled to the sensor array system through a common bus.The system also has a memory device 413 coupled to the processing deviceusing the bus.

Various instructions can be stored in the memory device or other memoryresources. In an example, the system has an instruction stored on thememory device. In an example, the instruction when executed by theprocessor causes the sensor array system to, as the AV travels a currentroute on a rail of the railway track system, dynamically detect areflectance of an event from a plurality of events, the event beingselected from an anomaly, a stationary feature, or a location of one ofthe other plurality of vehicles.

In an example, the system has a relationship table 415 comprising aplurality of sensor configurations for each respective one of aplurality of active sensor systems included in the sensor array systemto adjust within a time frame at least one output sensor configurationfor the sensor array to identify the event using the detectedreflectance of the event. In an example, the system also has an outputinterface 405. In an example, the system has an output interface totransmit an output to influence movement of the AV based upon thedetected reflectance and identified event as the AV travels the currentroute. Of course, there can be other variations, modifications, andalternatives.

Referring now to FIG. 5, the system includes a variety of commonelements of FIG. 4, which may be combined, replaced, or modified, in oneor more examples. As shown, the system has a sensor array systemconfigured spatially on the autonomous vehicle (AV). In an example, thesensor array system 403 comprises a plurality of active sensor systems409 and a plurality of passive sensor systems 407. In an example, atleast one processor device, such as a graphical processing unit 511, iscoupled to the sensor array system through a common bus. In an example,the graphical processing unit can be an NVIDIA DRIVE™ PX, which is theartificial intelligence (“AI”) car computer that enables automakers,truck makers, tier 1 suppliers, and startups to accelerate production ofautomated and autonomous vehicles. In an example, the unit scales from asingle processor configuration delivering auto cruise capabilities, to acombination of multiple processors and discrete GPUs designed to drivefully autonomous robot axis. Of course, the architecture is available ina variety of configurations ranging from one passively cooled mobileprocessor operating at 10 watts, to a multi-chip configuration with fourhigh performance AI processors—delivering 320 trillion deep learningoperations per second (TOPS)—that enable Level 5 autonomous driving.

In an example, the NVIDIA DRIVE PX platform combines deep learning,sensor fusion, and surround vision to change the driving experience. Theplatform is capable of understanding in real-time what's happeningaround the vehicle, precisely locating itself on an HD map, and planninga safe path forward. Designed around a diverse and redundant systemarchitecture, the NVIDIA DRIVE PX platform is built to support ASIL-D,the highest level of automotive functional safety. Further details ofthe platform from NVIDIA can be found at www.nvidia.com.

The system also has a memory device 413 coupled to the processing deviceusing the bus or other interface device. Various instructions can bestored in the memory device or other memory resources. In an example,the system has an instruction stored on the memory device. In anexample, the instruction when executed by the processor causes thesensor array system to, as the AV travels a current route on a rail ofthe railway track system, dynamically detect a reflectance of an eventfrom a plurality of events, the event being selected from an anomaly, astationary feature, or a location of one of the other plurality ofvehicles.

In an example, the system has a relationship table 415 comprising aplurality of sensor configurations for each respective one of aplurality of active sensor systems included in the sensor array systemto adjust within a time frame at least one output sensor configurationfor the sensor array to identify the event using the detectedreflectance of the event. In an example, the system also has an outputinterface 405. In an example, the system has an output interface totransmit an output to influence movement of the AV based upon thedetected reflectance and identified event as the AV travels the currentroute. Of course, there can be other variations, modifications, andalternatives.

In an example, the system also has an artificial intelligence module 501and a graphical processing unit 511. The artificial intelligence moduleis a neural net based process having a plurality of nodes, numbered from1 to N, where N is an integer greater than 100 or even 1,000,000, amongother variations. In an example, the graphical processing unit, can beone from Nvidia Corporation from Calif., among others.

Referring to FIG. 6, the present invention provides a simplifiedillustration 600 of a method for moving an autonomous vehicle among aplurality of vehicles configured on a railway track system. As shown,the method begins with start, 601. In an example, the method includesinitiating 603 movement of the autonomous vehicle configured in a railof the railway system. In an example, the vehicle begins movement 605 onthe track or other rail system. The method then begins monitoring 607 aplurality of parameters using a sensor array system, which has beendescribed above, and will be further described below.

In an example, the autonomous vehicle comprises a sensor array systemconfigured spatially on the autonomous vehicle (AV). In an example, thesensor array system comprises a plurality of active sensor systems. Thesystem has at least one processor device coupled to the sensor arraysystem. The system has a memory device coupled to the processing device.The system also has an instruction stored on the memory device, theinstruction when executed by the processor causes the sensor arraysystem to, as the AV travels a current route on the rail of the railwaytrack system, dynamically detect a reflectance of an event from aplurality of events, the event being selected from an anomaly, astationary feature, or a location of one of the other plurality ofvehicles. In an example, the system continues to monitor one or moreevents until a decision 609 is made as described below. As shown, thesystem has a “YES” branch to process 613, and a “NO” branch that pointsback to a step after initiation to restart the method in an example.

As shown in this example, the method includes using data from thereflectance of the event or the plurality of events to adjust 615 amovement of the AV in relationship to the event once the data has beenprocessed 613, while the AV is mechanically disconnected from theplurality of vehicles configured on the rail of the railway system orthe AV is mechanically connected to one or N−1 of the plurality ofvehicles numbered from 2 to N.

In an example, the method includes using a light-detection and ranging(LiDAR) system included in the sensor array system. In an example, theLiDAR is configured with the output sensor configuration to adjust oneor more of a scan rate of the LiDAR system, a photodetector sensitivityof the LiDAR system, or a laser power level of the LiDAR system.

The system has a relationship table comprising a plurality of sensorconfigurations for each respective one of a plurality of active sensorsystems included in the sensor array system to adjust within a timeframe at least one output sensor configuration for the sensor array toconclusively identify the event using the detected reflectance of theevent. The system also has an output to influence movement of the AVbased upon the detected reflectance and identified event as the AVtravels the current route. The system has a control system coupled tothe processor to dynamically processes data from the output derived fromthe sensor array system to increase speed, reduce speed, or stop the AValong the current route. As shown, the system also includes a stop step,621.

In an example, the railway system can be selected from a rail roadsystem, a trolley system, or other rail or fixed route system using arail or cables. Of course, there can be other variations, modifications,and alternatives.

In an example, sensor array system comprises a light-detection andranging (LiDAR) system. In an example, the output sensor configurationconfigures one or more of a scan rate of the LiDAR system, aphotodetector sensitivity of the LiDAR system, or a laser power level ofthe LiDAR system.

In an example, the sensor array system comprises a radar system. In anexample, the output sensor configuration configures a pulse width of acarrier signal of the radar system. In an example, each of the pluralityof active sensor systems emits one or more of sounds waves orelectromagnetic waves.

In an example, the system executed various instructions to adjustmovement of the vehicle. In an example, the executed instruction causesthe system to dynamically determine the one or more output sensorconfigurations by performing a lookup in the relationship table based ona reflectance of each of the detected reflectance events. In an example,the executed instruction cause the system to dynamically determine theone or more output sensor configurations by performing an optimizationutilizing a plurality of possible configurations for each of theplurality of active sensor systems based on a reflectance of each of thedetected reflectance events by identifying a surface feature of each ofthe events, and wherein the optimization uses a fitting function toconverge on the one or more sensor output configurations in therelationship table.

In other examples, the plurality of active sensor systems comprise aLiDAR system and a radar system. In an example, the sensor array furtherincludes a plurality of passive sensor systems that detect reflectednatural light. In an example, at least one of passive sensor systems ofthe sensor array comprises a stereo camera system. In an example, theLiDAR system, the radar system, and the stereo camera system eachprovides sensor data to a control system of the AV to enable the AV tomaneuver along the current route and initiate adjustment of the movementof the AV along the track.

In an example, the control system dynamically processes data from thesensor array system to increase speed, reduce speed, or stop the AValong the current route.

In other examples, the detected reflectance events comprise one or moresurface features of an environment around the AV and one or more weatherfeatures indicating precipitation. In an example, the executedinstruction further causes the predictive sensor array configurationsystem to: in response to identifying the weather features indicatingprecipitation, deprioritize sensor data from the LiDAR system forprocessing by the control system. In an example, the executedinstruction further causes the system to: in response to identifying theweather features indicating precipitation, deactivate the LiDAR system.In an example, the executed instruction causes the system to dynamicallyidentify the one or more reflectance events by receiving reflectancedata from a number of proximate AVs traveling on the current route onthe railway system.

In an example, the executed instruction causes the system to receive thereflectance data from the proximate AVs by establishing a mesh networkwith the proximate AVs on the railway system. In an example, theexecuted instruction further causes the system to: maintain a sub-mapdatabase comprising 3D surface data of an operational region of the AVon the railway system; and identify, using a current position of the AV,a correlated sub-map from the sub-map database that provides 3D surfacedata surrounding the current position of the AV. In an example, theexecuted instruction causes the system to dynamically identify thereflectance events that affect detectability by the sensor array fromthe 3D surface data provided by the correlated sub-map.

In an example, the stationary feature being one or more of a railwaysign, a railway station, a railway track, a vehicle roadway, a railwaycrossing, or other fixture, the location of one of the other pluralityof vehicles comprising a distance between the other vehicle and the AV,the anomaly being one or more of a human being, a dog, a cat, a horse,cattle, a moving vehicle crossing the railway track, a weathercondition, or a defect on the railway track. Of course, there can beother variations, modifications, and alternatives.

Having described various embodiments, examples, and implementations, itshould be apparent to those skilled in the relevant art that theforegoing is illustrative only and not limiting, having been presentedby way of example only. Many other schemes for distributing functionsamong the various functional elements of the illustrated embodiment orexample are possible. The functions of any element may be carried out invarious ways in alternative embodiments or examples.

Also, the functions of several elements may, in alternative embodimentsor examples, be carried out by fewer, or a single, element. Similarly,in some embodiments, any functional element may perform fewer, ordifferent, operations than those described with respect to theillustrated embodiment or example. Also, functional elements shown asdistinct for purposes of illustration may be incorporated within otherfunctional elements in a particular implementation. Also, the sequencingof functions or portions of functions generally may be altered. Certainfunctional elements, files, data structures, and so one may be describedin the illustrated embodiments as located in system memory of aparticular or hub. In other embodiments, however, they may be locatedon, or distributed across, systems or other platforms that areco-located and/or remote from each other. For example, any one or moreof data files or data structures described as co-located on and “local”to a server or other computer may be located in a computer system orsystems remote from the server. In addition, it will be understood bythose skilled in the relevant art that control and data flows betweenand among functional elements and various data structures may vary inmany ways from the control and data flows described above or indocuments incorporated by reference herein. More particularly,intermediary functional elements may direct control or data flows, andthe functions of various elements may be combined, divided, or otherwiserearranged to allow parallel processing or for other reasons. Also,intermediate data structures of files may be used and various describeddata structures of files may be combined or otherwise arranged.

In other examples, combinations or sub-combinations of the abovedisclosed invention can be advantageously made. The block diagrams ofthe architecture and flow charts are grouped for ease of understanding.However, it should be understood that combinations of blocks, additionsof new blocks, re-arrangement of blocks, and the like are contemplatedin alternative embodiments of the present invention.

Examples of processing techniques and systems can be found in U.S. Pat.No. 9,841,763 issued Dec. 12, 2017, and titled “Predictive Sensor ArrayConfiguration System for an Autonomous Vehicle,” which is incorporatedby reference herein.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

What is claimed is:
 1. A method for moving an autonomous vehicle (AV)among a plurality of vehicles configured on a railway system, the methodcomprising: initiating movement of the AV configured in a rail of therailway system, the autonomous vehicle comprising a sensor array systemconfigured spatially on the AV, the sensor array system comprising aplurality of active sensor systems; at least one processor devicecoupled to the sensor array system; a memory device coupled to theprocessing device; an instruction stored on the memory device, theinstruction when executed by the processor causes the sensor arraysystem to, as the AV travels a current route on the rail of the railwaysystem, dynamically detect a reflectance of an event from a plurality ofevents, the event being selected from an anomaly, a stationary feature,or a location of one of a plurality of vehicles configured on the railof the railway system; using data from the reflectance of the event orthe plurality of events to adjust a movement of the AV in relationshipto the event, while the AV is mechanically disconnected from theplurality of vehicles configured on the rail of the railway system;monitoring each of the AVs using a central database in real time, whilecollecting information associated each AVs in the central database; andusing a cell phone to communicate with the AV to contact the AV for apickup by a user before the initiating movement of the AV configured onthe rail of the railway system.
 2. The method of claim 1, wherein the AVfurther comprises: a relationship table comprising a plurality of sensorconfigurations for each respective one of a plurality of active sensorsystems included in the sensor array system to adjust within a timeframe one or more output sensor configurations for the sensor array toidentify the event using the detected reflectance of the event; and anoutput to influence movement of the AV based upon the detectedreflectance and identified event as the AV travels the current route. 3.The method of claim 2, wherein the sensor array system further comprisesa light-detection and ranging (LiDAR) system, and wherein the outputsensor configuration configures one or more of a scan rate of the LiDARsystem, a photodetector sensitivity of the LiDAR system, or a laserpower level of the LiDAR system.
 4. The method of claim 2, wherein thesensor array system further comprises a radar system, and wherein theoutput sensor configuration configures a pulse width of a carrier signalof the radar system.
 5. The method of claim 2, wherein each of theplurality of active sensor systems emits one or more of sounds waves orelectromagnetic waves.
 6. The method of claim 2, wherein the executedinstruction causes the system to dynamically determine the one or moreoutput sensor configurations by performing a lookup in the relationshiptable based on a reflectance of each of the detected reflectance events.7. The method of claim 2, wherein the executed instruction cause thesystem to dynamically determine the one or more output sensorconfigurations by performing an optimization utilizing a plurality ofpossible configurations for each of the plurality of active sensorsystems based on a reflectance of each of the detected reflectanceevents by identifying a surface feature of each of the events, andwherein the optimization uses a fitting function to converge on the oneor more sensor output configurations in the relationship table.
 8. Themethod of claim 2 wherein the plurality of active sensor systemscomprise a light-detection and ranging (LiDAR) system and a radarsystem, and wherein the sensor array system further includes a pluralityof passive sensor systems that detect reflected natural light.
 9. Themethod of claim 8, wherein the plurality of passive sensor systems ofthe sensor array system comprise a stereo camera system.
 10. The methodof claim 9 wherein the LiDAR system, the radar system, and the stereocamera system each provides sensor data to a control system of the AV toenable the AV to maneuver along the current route and initiateadjustment of the movement of the AV along a track.
 11. The method ofclaim 10, wherein the control system dynamically processes data from thesensor array system to increase speed, reduce speed, or stop the AValong the current route or wherein the control system interfaces withthe central database to increase speed, reduce speed, or stop the AValong the current route.
 12. The method of claim 11, wherein thedetected reflectance events comprise one or more surface features of anenvironment around the AV and one or more weather features indicatingprecipitation.
 13. The method of claim 12, wherein the executedinstruction further causes the predictive sensor array configurationsystem to: in response to identifying the weather features indicatingprecipitation, deprioritize sensor data from the LiDAR system forprocessing by the control system.
 14. The method of claim 12, whereinthe executed instruction further causes the system to: in response toidentifying the weather features indicating precipitation, deactivatethe LiDAR system.
 15. The method of claim 2, wherein the executedinstruction causes the system to dynamically identify the one or morereflectance events by receiving reflectance data from a number ofproximate AVs traveling on the current route on the railway system. 16.The method of claim 15, wherein the executed instruction causes thesystem to receive the reflectance data from the proximate AVs byestablishing a mesh network with the proximate AVs on the railway systemor other entities that are not AVs.
 17. The method of claim 2, whereinthe executed instruction further causes the system to: maintain asub-map database comprising 3D surface data of an operational region ofthe AV on the railway system; and identify, using a current position ofthe AV, a correlated sub-map from the sub-map database that provides 3Dsurface data surrounding the current position of the AV; wherein theexecuted instruction causes the system to dynamically identify thereflectance events that affect detectability by the sensor array fromthe 3D surface data provided by the correlated sub-map.
 18. The methodof claim 2, wherein the stationary feature is one or more of a railwaysign, a railway station, a railway track, a vehicle roadway, a railwaycrossing, or other fixture, the location of one of the other pluralityof vehicles comprising a distance between the other vehicle and the AV,the anomaly being one or more of a human being, a dog, a cat, a horse,cattle, a moving vehicle crossing the railway track, a weathercondition, or a defect on the railway track.
 19. A method for initiatinga call from a user using a network and moving an autonomous vehicle (AV)among a plurality of vehicles configured on a railway system, the methodcomprising: initiating movement of the AV configured in a rail of therailway system, the autonomous vehicle comprising a sensor array systemconfigured spatially on the AV, the sensor array system comprising aplurality of active sensor systems; at least one processor devicecoupled to the sensor array system; a memory device coupled to theprocessing device; an instruction stored on the memory device, theinstruction when executed by the processor causes the sensor arraysystem to, as the AV travels a current route on the rail of the railwaysystem, dynamically detect a reflectance of an event from a plurality ofevents, the event being selected from an anomaly, a stationary feature,or a location of one of a plurality of other vehicles; and using datafrom the reflectance of the event or the plurality of events to adjust amovement of the AV in relationship to the event, while the AV ismechanically disconnected from the plurality of other vehiclesconfigured on the rail of the railway system or the AV is mechanicallyconnected to one or N−1 of the plurality of vehicles numbered from 2 toN.
 20. The method of claim 19, wherein the sensor array system furthercomprises a light-detection and ranging (LiDAR) system, the LiDAR beingconfigured to adjust one or more of a scan rate of the LiDAR system, aphotodetector sensitivity of the LiDAR system, or a laser power level ofthe LiDAR system, and wherein the AV further comprises a relationshiptable comprising a plurality of sensor configurations for eachrespective one of a plurality of active sensor systems included in thesensor array system to adjust within a time frame at least one outputsensor configuration for the sensor array to conclusively identify theevent using the detected reflectance of the event; an output toinfluence movement of the AV based upon the detected reflectance andidentified event as the AV travels the current route; and a controlsystem coupled to the processor to dynamically processes data from theoutput derived from the sensor array system to increase speed, reducespeed, or stop the AV along the current route.
 21. The method of claim19, wherein the railway system can be selected from a rail road system,a trolley system, or other rail or fixed route system using a rail orcables.
 22. A system for initiating movement and adjusting the movementof an autonomous vehicle (AV) configured on a rail of a railway system,the system comprising: a plurality of vehicles configured on the rail ofthe railway system and the autonomous vehicle (“AV”) configured on arail of the railway system, and mechanically detached from the pluralityof vehicles, the AV comprising: a sensor array system configuredspatially on the AV, the sensor array system comprising a plurality ofactive sensor systems; at least one processor device coupled to thesensor array system; a memory device coupled to the processing device;an instruction stored on the memory device, the instruction whenexecuted by the processor causes the sensor array system to, as the AVtravels a current route on a rail of the railway system, dynamicallydetect a reflectance of an event from a plurality of events, the eventbeing selected from an anomaly, a stationary feature, or a location ofone of a plurality of vehicles; a relationship table comprising aplurality of sensor configurations for each respective one of theplurality of active sensor systems included in the sensor array systemto adjust within a time frame at least one output sensor configurationsfor the sensor array to identify the event using the detectedreflectance of the event; and an output to influence movement of the AVbased upon the detected reflectance and identified event as the AVtravels the current route; and an application configured on a cell phonecoupled to a networking system, the application on the cell phone of theuser provided to initiate pickup of the user using the AV.
 23. Thesystem of claim 22, wherein the sensor array system comprises alight-detection and ranging (LiDAR) system, and wherein the outputsensor configuration configures one or more of a scan rate of the LiDARsystem, a photodetector sensitivity of the LiDAR system, or a laserpower level of the LiDAR system.
 24. The system of claim 22, wherein thesensor array system comprises a radar system, and wherein the outputsensor configuration configures a pulse width of a carrier signal of theradar system.
 25. The system of claim 22, wherein each of the pluralityof active sensor systems emits one or more of sounds waves orelectromagnetic waves.
 26. The system of claim 22, wherein the executedinstruction causes the system to dynamically determine the at least oneoutput sensor configuration by performing a lookup in the relationshiptable based on a reflectance of each of the detected reflectance events.27. The system of claim 22, wherein the executed instruction cause thesystem to dynamically determine the at least one output sensorconfiguration by performing an optimization utilizing a plurality ofpossible configurations for each of the plurality of active sensorsystems based on a reflectance of each of the detected reflectanceevents by identifying a surface feature of each of the events, andwherein the optimization uses a fitting function to converge on the atleast one sensor output configuration in the relationship table.
 28. Thesystem of claim 22, wherein the plurality of active sensor systemscomprise a light-detection and ranging (LiDAR) system and a radarsystem, and wherein the sensor array system further includes a pluralityof passive sensor systems that detect reflected natural light.
 29. Thesystem of claim 22, wherein the plurality of passive sensor systems ofthe sensor array system comprise a stereo camera system.
 30. The systemof claim 22, wherein the LiDAR system, the radar system, and the stereocamera system each provides sensor data to a control system of the AV toenable the AV to maneuver along the current route and initiateadjustment of the movement of the AV along a track.
 31. The system ofclaim 22, wherein the control system dynamically processes data from thesensor array system to increase speed, reduce speed, or stop the AValong the current route.
 32. The system of claim 22, wherein thedetected reflectance events comprise one or more surface features of anenvironment around the AV and one or more weather features indicatingprecipitation.
 33. The system of claim 22, wherein the executedinstruction further causes the predictive sensor array configurationsystem to: in response to identifying the weather features indicatingprecipitation, deprioritize sensor data from the LiDAR system forprocessing by the control system.
 34. The system of claim 33, whereinthe executed instruction further causes the system to: in response toidentifying the weather features indicating precipitation, deactivatethe LiDAR system.
 35. The system of claim 34, wherein the executedinstruction causes the system to dynamically identify the one or morereflectance events by receiving reflectance data from a number ofproximate AVs traveling on the current route on the railway system. 36.The system of claim 35, wherein the executed instruction causes thesystem to receive the reflectance data from the proximate AVs byestablishing a mesh network with the proximate AVs on the railwaysystem.
 37. The system of claim 22, wherein the executed instructionfurther causes the system to: maintain a sub-map database comprising 3Dsurface data of an operational region of the AV on the railway system;and identify, using a current position of the AV, a correlated sub-mapfrom the sub-map database that provides 3D surface data surrounding thecurrent position of the AV; wherein the executed instruction causes thesystem to dynamically identify the reflectance events that affectdetectability by the sensor array from the 3D surface data provided bythe correlated sub-map.
 38. The system of claim 22, wherein thestationary feature is one or more of a railway sign, a railway station,a railway track, a vehicle roadway, a railway crossing, or otherfixture, the location of one of the other plurality of vehiclescomprising a distance between one of the plurality of vehicle and theAV, the anomaly being one or more of a human being, a dog, a cat, ahorse, cattle, a moving vehicle crossing the railway track, a weathercondition, or a defect on the railway track.